Solving AI Model Marginal Cost Issues

Profit margins arguably are the key business issue for frontier artificial intelligence model providers.

Where software businesses have tended to have low marginal costs, with high profit margins at scale, AI businesses tend to have no such advantage. 

Inference and training have heavily variable costs tied to usage and scale. In other words, cost per query scales mostly in linear fashion. 

That is quite unlike traditional software, with 80 percent to 90 percent gross margins at scale, since marginal cost is quite low. 

And that might have important ramifications for pricing models.

Traditional software can often support very high gross margins because the marginal cost of serving one more user is close to zero, while AI products can have materially higher variable costs because each additional query or token consumes compute.

That difference pushes AI businesses toward pricing and operating models that track usage, outcomes, or hybrid bundles rather than pure seat-based subscriptions.

For classic software, once the product is built, distributing another copy is cheap, so revenue can scale much faster than cost. 

In AI, each extra interaction may add inference, context, storage, and orchestration costs, so the economics can look more like a utility or metered service than a pure software license.

That does not mean AI cannot be highly profitable; it means profitability depends more on model efficiency, pricing design, and control over the inference stack.

The emerging pattern is hybrid monetization mixing flat rate charges and usage:

• subscriptions for baseline access

• usage-based credits

• metered throughput

• outcome-based pricing for heavier AI usage.

If additional usage carries significant marginal cost, sustainable AI business models usually combine four elements: 

• cost-aware product design (cost per interaction)

• pricing aligned to actual usage intensity (tiered pricing)

• infrastructure leverage (own rather than rent)

• AI feature packaging (proprietary data, workflow integration). 

A practical way to think about it is this: the business must make sure marginal revenue per interaction stays above marginal cost per interaction, with enough spread to fund sales, support, R&D, and model improvement. 

That often means charging more for complex tasks, throttling or downgrading expensive requests, or bundling some usage into plans while monetizing power users separately.

Issue	Why it matters	Possible solution
High per-use compute cost	Margins can compress as usage rises	Tiered pricing, credits, or metered billing tied to task intensity
Third-party model dependence	Vendor price changes and rate limits can hit gross margin	Own more of the inference stack, add model abstraction, or use open-source/smaller models where feasible
Weak price predictability	Customers dislike uncertain AI bills, slowing adoption	Pre-commits, usage caps, transparent dashboards, and clear credit buckets
Flat seat pricing mismatch	Power users can consume far more than average users	Hybrid subscription plus usage-based overages or action-based pricing
Hard-to-measure outcomes	Outcome pricing is difficult to verify and negotiate	Start with activity-based metrics, move to outcomes only where measurable and automated
Scaling costs faster than revenue	Growth can worsen economics instead of improving them	Cost monitoring, workload routing, batch inference, and feature-level margin analysis
Commoditization risk	Models alone are easy to copy	Build proprietary data, vertical specialization, and workflow integration

AI winners are more likely to be firms that control costs, can manage usage and price in a way that reflects both value delivered and the true marginal cost of serving that value.

Anthropic Fable 5: a Marketing Platform Gone Wrong

Has Anthropic poisoned its own well by arguing its new Mythos model was so dangerous it couldn’t be released publicly?

After Amazon researchers discovered a jailbreak of Fable 5 (Mythos with guardrails), the U.S. government banned use of both Mythos and Fable 5, exactly the kind of government scrutiny that could disrupt Anthropic’s  business most.

Amazon CEO Andy Jassy was reportedly the source of security concerns that led the U.S. government to force Anthropic to shut down Fable 5 and Mythos 5 for every customer, also imposing an export control ban on both models.

Oddly enough, Amazon is one of Anthropic’s largest investors, having put in billions and receiving a $100 billion cloud spending commitment in return. 

Anthropic says the jailbreak technique surfaced “a small number of previously known, minor vulnerabilities.” 

It called the government’s response disproportionate and said the capabilities causing concern are already available in other publicly accessible models. The shutdown affected every customer globally because Anthropic cannot filter foreign nationals from U.S. users in real time.

For Anthropic, the immediate question is how quickly it can restore access. And that will happen.

For the broader industry, the precedent is what matters. Anthropic has been lobbying for more regulatory power over AI. It just found out it might get what it wants. 

Anthropic's own CEO publicly called for the government to have authority to block dangerous model releases. The government then used such calls against Anthropic two days later.

Optimists might argue that government restrictions on dual-use technologies might slow down technology diffusion, but cannot halt it.

The general pattern across technology history is this: governments impose controls with genuine security rationale, those controls prove partially effective in the short term, generate substantial unintended costs, and are eventually relaxed or circumvented.

The regulated party frequently bears costs that accrue to unregulated competitors.

Technology	Regulation	Period	Short-Term Effect	Long-Term Outcome
Cryptography, Encryption (PGP, SSL)	U.S. export ban; treated as a munition under ITAR; Clipper Chip mandate attempt	1975 to 2000	Phil Zimmermann prosecuted 3 years for posting PGP online; RSA export restricted New America · Brookings	Controls collapsed when it became obvious software could not be bottled; Clinton-era deregulation followed. In subsequent decades, virtually all predictions about encryption's economic benefits were borne out — SSL, SSH, electronic banking, e-filing, and VPNs depended on exactly the strong encryption the government tried to restrict.
Cryptography	Export controls treated software as equivalent to munitions	1990s	The Zimmermann case dragged on three years before being dropped, and export control laws were eventually rewritten after it became obvious that software couldn't be contained like rocket motors. Crowe LLP · Wassenaar/arxiv	The EU abolished cryptographic export controls within the union in 2000, a decision subsequently adopted by the Clinton administration; more recent trends have involved further relaxation rather than tighter controls.
CoCom, Cold War Computing	Coordinating Committee for Multilateral Export Controls: Western embargo on technology exports to Soviet Bloc	1949–1994	In the period 1951–1967 CoCom performed reasonably well despite its known limits. After that, enforcement eroded. ChinaTalk/Ottinger · Texas Nat'l Security Review	CoCom's effectiveness was reduced throughout its history by overt non-compliance, differences between member nations, the secretive regime, and the financial incentives of tech exporters. CoCom did not prevent the USSR from accessing key technologies; the size of the control regime expanded over time, weakening enforcement and encouraging defection. Disbanded 1994.
Toshiba-Kongsberg Scandal	Sanctions on Toshiba for selling machine tools enabling quieter Soviet submarine propellers	1982–1987	Washington imposed sanctions on Toshiba worth $30 billion amid congressional claims the breach jeopardized U.S. national security. AFSA	The $30 billion estimate was primarily based on a hyperbolic claim that the entire U.S. nuclear submarine fleet would need to be replaced — the actual magnitude of the damage remained unclear. Sanctions were eventually relaxed; Toshiba survived.
Nuclear Technology	Nuclear Non-Proliferation Treaty (NPT); Nuclear Suppliers Group; export controls on enrichment technology	1968–present	By the 1950s, it became clear that pure denial had neither stopped the Soviet Union nor the UK from acquiring nuclear weapons. The 1954 revision of the Atomic Energy Act reflected a shift from prevention-through-denial to influence-through-cooperation. CNAS · AADS	U.S. dominance in commercial nuclear energy in the 1970s allowed the U.S. to keep Taiwan and South Korea from developing their own nuclear facilities — a genuine success. But Pakistan, India, North Korea, and Israel all acquired nuclear capability despite controls. NPT remains the most durable tech-control regime, though partial.
Semiconductors, AI Chips (China)	BIS October 2022 controls on NVIDIA A100/H100 and manufacturing equipment; tightened 2023, 2024, 2025	2022–present	Implementation significantly disrupted China's semiconductor ecosystem, causing price spikes for some device types and forcing workforce reductions. CSIS/HSToday · CSIS	Loopholes, alternative approaches, and unforeseen outcomes have diminished long-term efficacy — the very restrictions designed to hinder Beijing may instead be accelerating China's domestic progress. Companies impacted by the October 2022 controls, on average, even outperformed comparable unaffected companies, exhibiting higher increases in R&D spending and patent filings — a counterintuitive outcome driven by AI chip demand.
Semiconductor Controls	EAR controls tightened repeatedly; allied coordination with Netherlands, Japan	2022–present	According to research, versions of Intel's Xeon Gold and NVIDIA GeForce RTX chips can be bought on Taobao; students at Tsinghua report being able to "easily circumvent" restrictions on U.S. EDA software. Texas NSR	CoCom did not prevent the USSR from accessing key technologies; the current regime is similarly porous, and China is a more adept target. Long-term verdict still open.
AI Models Fable 5 / Mythos 5	U.S. Commerce Dept. emergency export control directive; all foreign nationals barred	June 2026	Anthropic had to cut off access to both models for all customers worldwide because it could not rapidly implement nationality-based filtering. Anthropic disputed the severity of the jailbreak, arguing many of the same vulnerabilities could be discovered using other publicly available models. Fortune · Axios · State of Surveillance	Too recent to assess long-term outcome. The next letter may go to a different vendor; the policy tools used here will be available to every future administration. Watch for restored access accompanied by new conditions such as nationality verification, tighter pre-deployment review, or federal observer access.

Several durable findings emerge from this body of evidence:

• Unilateral controls erode without multilateral coordination

• The control regime tends to expand and become self-defeating

• Denial often accelerates indigenous capability in the target

• Software and information resist containment

• The regulated party bears costs; competitors do not. 

Rival Sam Altman, of OpenAI, says Anthropic’s fear-based marketing now has come back to bite Anthropic. 

AI Model Pricing Eventually Moves to a Cloud Model

So far, language model pricing based on usage closely mirrors the early patterns of cloud computing, .

Which matured into a massive, lower-margin commodity market with heavy optimization practices.

AI inference is on a similar but possibly accelerated trajectory.

We might reasonably expect:

• Price deflation and commoditization

• More sophisticated pricing models

• Shift to agentic operations

• Hybrid platforms

• More cost governance measures.

source: Rohan Modi 

It is reasonable to expect prices for baseline capabilities will fall (potentially another 5-10x over years).

Frontier capabilities will remain premium but get cheaper over time, as well.

We probably should see "good enough" models for most tasks, mirroring how basic cloud virtual machines became very affordable.

Reserved-like plans, volume discounts and sustained use discounts already are emerging, with more pricing granularity. 

The shift from chatbots to agents will likely lead to pricing based on:

• Per-agent/subscription (like "digital employee" salaries)

• Outcomes (pay per resolved ticket, qualified lead, successful workflow)

• Hybrid models (base + usage + performance bonuses).

Overall, the shift is from pricing based on “raw compute” to “managed services” with value-based elements.

source: Rohan Modi 

Overall, expect AI pricing to become more predictable, value-aligned, and cheaper per capability unit, much as cloud computing evolved. 

SpaceX has Gone Public

SpaceX has completed the largest initial public offering in history, raising $75 billion. The listing priced 555.6 million shares at $135 each, granting the company a mammoth market valuation of $1.77 trillion.

source: Bret Jensen 

Source: SpaceX, the Verge 

source: SpaceX, Space News 

And even if Starlink powers today’s revenue, the future upside is clearly pegged to artificial intelligence, according to SpaceX itself. 

Will the 2026 World Cup Create Any Long-Term Economic Benefit for Host Nations?

World Cup long-term economic effects will be negligible, economists at Goldman Sachs say. That might seem unlikely, given the 2026 FIFA World Cup featuring 48 teams and 104 matches across the United States, Canada and Mexico.

After Goldman Sachs International economists Kevin Daly and Mambuna Njie studied gross domestic product data covering every World Cup since 1982, they find hosting produces a marginally positive but statistically insignificant effect on real output, with long-run impact that is effectively zero.

FIFA and the World Trade Organization disagree.  A joint study they published in April 2025, developed by consultancy OpenEconomics, projects a $17.2 billion contribution to U.S. GDP, $30.5 billion in gross output and approximately 185,000 full-time equivalent jobs for the host country alone. 

Across all three host countries, the combined GDP estimate reaches $40.9 billion, the report argues. 

Different methodologies help explain the differences. 

Beer, merchandise and apparel purchased in their own markets does not register in U.S., Canadian or Mexican GDP. 

Domestic spending on World Cup-related goods and services may simply be redirected from other consumption categories rather than representing new activity.

There is short-term lift, but no lasting contribution.

Leakage effects also are real: profits from international licensing, sponsorship and supply chains accrue outside the host country’s GDP.

On the other hand, it stands to reason that several industries should benefit, including:

• European and US consumer staples (brewing companies including AB InBev, Molson Coors, Constellation Brands, Heineken and Carlsberg)

• European consumer discretionary, primarily sportswear (the ones we know: adidas, PUMA)

• U.S. retail and softlines (Academy Sports + Outdoors, Dick’s Sporting Goods, Nike)

• U.S. lodging and leisure (Hyatt, Marriott, Hilton, Airbnb)

• U.S. airlines. 

There will be significant industrial impact in those segments of the market, to be sure. Concentrated, time limited but real. 

Wider and long-term benefits will likely be negligible, if measurable at all. 

In many ways, the impact is similar to that supposedly created by municipally-financed sports stadia. 

The claim that government-financed sports stadia act as engines for economic growth is widely contested within the field of economics. 

While proponents often cite job creation, increased tax revenues, and regional prestige as primary justifications for public subsidies, empirical research consistently demonstrates that these facilities rarely produce significant, measurable net economic benefits for host cities.

The core economic argument against public financing centers on the substitution effect. 

Economic models often fail to account for the fact that a large portion of spending at a stadium is not "new" money introduced into the local economy; rather, it is money that residents would have otherwise spent on other local entertainment options, such as restaurants, movie theaters, or other cultural activities. 

Because this spending is simply redirected, there is little to no net increase in total local economic activity.

Furthermore, economic impact studies commissioned by proponents often rely on flawed multipliers that exaggerate the stimulative effect of sports expenditures. 

These studies frequently ignore "leakage," where significant portions of the revenue (such as players' salaries) are exported out of the local economy because athletes and owners often do not reside in the city where they play. 

Consequently, most independent academic research concludes that the public cost of these subsidies far exceeds any marginal economic growth they may stimulate.

Study / Authors	Findings	Source
Bradbury, Coates, & Humphreys (2023)	Retrospective analysis confirming that stadiums are poor public investments and that public outlays provide meager benefits.	Link
Matheson (2018)	Found no evidence that stadium subsidies yield economic growth; suggested that at most 5–15% of public cost might be justified by "public good" (civic pride).	Link
Bradbury (2022)	Found negligible net increases in sales tax collections and noted that approximately one-third of stadium sales displace other local activity.	Link
Siegfried & Zimbalist (2002)	Demonstrated that standard impact multipliers exaggerate benefits by over 400% due to consumer substitution and economic leakage.	Link
Coates & Humphreys (2003)	Econometric analysis finding no evidence of positive economic benefits associated with stadium construction; some results indicated negative impacts.	Link